Life

BackgroundThe coronavirus disease 2019 (COVID-19) pandemic globally changed respiratory infection patterns. However, its impact on community-acquired pneumonia (CAP) in high risk patients with haematological malignancies (HM) is uncertain. We aimed to examine CAP aetiology changes in patients with HM pre- and post-COVID-19 pandemic. MethodsThis retrospective study included 524 HM patients hospitalised with CAP between March 2018 and February 2022. Those who underwent bronchoscopy within 24 hours after admission to identify CAP aetiology were included. Data on patient characteristics, laboratory findings, and results of bronchioalveolar lavage fluid cultures and PCR tests were analysed to compare etiological changes and identify in-hospital mortality risk factors. ResultsPatients were divided into pre-COVID-19 (44.5%) and post-COVID-19 (55.5%) groups. This study found a significant decrease in viral CAP in the post-COVID-19 era, particularly for influenza A, parainfluenza, adenovirus, and rhinovirus (3.0% vs. 0.3%, respectively, P = 0.036; 6.5% vs. 0.7%, respectively, P = 0.001; 5.6% vs. 1.4%, respectively, P = 0.015; 9.5% vs. 1.7%, respectively, P < 0.001). Bacterial, fungal, and unknown CAP aetiologies remain unchanged. Higher Sequential Organ Failure Assessment scores and lower platelet count correlated with in-hospital mortality after adjusting for potential confounding factors. ConclusionThe incidence of CAP in HM patients did not decrease after COVID-19. Additionally, CAP aetiology among patients with HM changed following the COVID-19 pandemic, with a significant reduction in viral pneumonia while bacterial and fungal pneumonia persisted. Further studies are required to evaluate the impact of COVID-19 on the prognosis of patients with HM and CAP.

There is paucity of evidence related to mechanical ventilation in the general ward setting. We aimed to study the clinico-etiological profile, outcomes and prognostic factors of patients receiving invasive mechanical ventilation in non-ICU (ward) setting, and compare these parameters with that of patients in the ICU, wherever it was reported. A systematic review and meta-analysis was done on articles published till June 2020. Two authors independently extracted the data. The study population included patients who received mechanical ventilation in ward setting. Fourteen studies reporting on 20833 patients were included (20252 exclusively ventilated in ward), with most of the studies being from Israel, USA, Japan and Taiwan. Risk of bias was estimated using the Newcastle-Ottawa Scale for observational studies, and was found to be low. Most common reason for intubation was respiratory illness. Most common variables predicting mortality were prognostic scores like APACHE-II and Acute Physiology Score (APS). Pooled mortality rate in ward across 6 studies was 0.72 (95% CI 0.69-0.74) with no heterogeneity among these 6 studies (I2=0.0). Mortality rate varied significantly with study population characteristics, and was lower among patients being weaned in ward. A major limitation of our study was the paucity of studies and significant heterogeneity among existing studies, with respect to outcomes like duration of ventilation, hospital stay, rates of complications, and prognostic factors. This systematic review and meta-analysis found that mortality among patients receiving invasive mechanical ventilation in ward settings remains high. Data regarding other outcomes and prognostic factors predicting mortality was very heterogeneous highlighting the need for future studies concentrating specifically on these aspects. Systematic review registration: PROSPERO 2020 (CRD42020166775)

Studying the ICNARC Case Mix Programme Database has yielded results showing intensive care admissions by those infected with COVID-19 have twice the corrected mortality of patients presenting with non-COVID-19 viral pneumonia. A basis of an APACHE-II-like score denoted as "BASCA" is also outlined in this study.

earlier and longer corticosteroid therapy with methylprednisolone could reduce the mortality and/or rate of ICU admission by 26% in patients with severe COVID-19, hospitalized in conventional medical ward. BackgroundLow dose of dexamethasone reduced mortality in hospitalized COVID-19 patients who required respiratory support. Less is known about the efficacy of other corticosteroids in severe COVID-19 patients. This study was designed to determine if longer and earlier corticoid therapy in severe COVID-19 patients is associated with reduced mortality and/or reduced rate of ICU admission for worsening of respiratory state. MethodsWe performed a retrospective study with patients aged [&ge;] 18 years, with epidemiological and/or radiological suspected COVID-19, hospitalized in a regional hospital GHRMSA Mulhouse, France. Twenty-three patients received methylprednisolone (MP) as compassionate use, 1 mg/kg/day for seven days followed by prednisolone at a gradually reduced dosage, for 4 to 6 weeks. MP was started one week after COVID-19 symptoms first appeared. The primary composite outcome was mortality and/or ICU admission during hospitalisation. ResultsBetween March 14th to June 5th 2020, 255 patients were screened, 181 were included, and 92 were analysed, 23 patients treated with MP and 69 received standard care. SARS-CoV2 infection was confirmed by reverse transcriptase polymerase chain reaction (RT-PCR) in 82.3%. The overall composite outcome was higher in the control group: 42/69 patients (60.9%) versus 8/23 (34.8%) in the interventional group (p= 0.018). The correction of lymphopenia between days 1 to 7 was associated with better outcome (p=0.006). ConclusionThese results suggest that earlier and longer corticosteroid therapy with methylprednisolone could reduce the mortality and/or rate of ICU admission in patients with severe COVID-19, hospitalized in conventional medical ward.

BackgroundThe COVID-19 pandemic has been a watershed event. Industry and governments have reacted, investing over US$105 billion in vaccine research.1 The Holy Grail is a universal, pan-coronavirus, vaccine to protect humankind from future SARS-CoV-2 variants and the thousands of similar coronaviruses with pandemic potential.2 This paper proposes a new vaccine candidate that appears to attenuate the SARS-Cov-2 coronavirus variants to render it safe to use as a vaccine. Moreover, these results indicate it may be efficacious against 99% of 365 coronaviruses. This research model is wet-dry-wet; it originated in genomic sequencing laboratories, evolved to computational modeling, and the candidate result now require validation back in a wet lab. ObjectivesThis studys purpose was to test the hypothesis that machine learning applied to sequenced coronaviruses genomes could identify which amino acid substitutions likely attenuate the viruses to produce a safe and effective pan-coronavirus vaccine candidate. This candidate is now eligible to be pre-clinically then clinically tested and proven. If validated, it would constitute a traditional attenuated virus vaccine to protect against hundreds of coronaviruses, including the many future variants of SARS-CoV-2 predicted from continuously recombining in unvaccinated populations and spreading by modern mass travel. MethodsUsing machine learning, this was an in silico comparative effectiveness research study on trinucleotide functions in nonstructural proteins of 365 novel coronavirus genomes. Sequences of 7,097 codons in the ORF1ab gene were collected from 65 global locations infecting 68 species and reported to the US National Institute of Health. The data were proprietarily transformed twice to enable machine learning ingestion, mapping, and interpretation. The set of 2,590,405 data points was randomly divided into three cohorts: 255 (70%) observations for training; and two cohorts of 55 (15%) observations each for testing. Machine learning models were trained in the statistical programming language R and compared to identify which mixture of the 7.097 x 1023 possible amino-acid-location combinations would attenuate SARS-CoV-2 and other coronaviruses that have infected humans. ResultsContests of machine-learning algorithms identified nine amino-acid point substitutions in the ORF1ab gene that likely attenuate 98.98% of 365 (361) novel coronaviruses. Notably, seven substitutions are for the amino acid alanine. Most of the locations (5 of 9) are in nonstructural proteins (NSPs) 2 and 3. The substitutions are alanine to (1) valine at codon 4273; (2) leucine at codon 5077; (3) phenylalanine at codon 2001; (4) leucine at codon 372; (5) proline at codon 354; (6) phenylalanine at codon 2811; (7) phenylalanine at codon 4703; (8) leucine to serine at codon 2333; and, (9) threonine to alanine at codon 5131. ConclusionsThe primary outcome is a new, highly promising, pan-coronavirus vaccine candidate based on nine amino-acid substitutions in the ORF1ab gene. The secondary outcome was evidence that sequences of wet-dry lab collaborations - here machine learning analysis of viral genomes informing codon functions -- may discover new broader and more stable vaccines candidates more quickly and inexpensively than traditional methods.

There is limited information describing features and outcomes of patients requiring hospitalization for COVID19 disease and still no treatments have clearly demonstrated efficacy. Demographics and clinical variables on admission, as well as laboratory markers and therapeutic interventions were extracted from electronic Clinical Records (eCR) in 4712 SARS-CoV2 infected patients attending 4 public Hospitals in Madrid. Patients were stratified according to age and stage of severity. Using multivariate logistic regression analysis, cut-off points that best discriminated mortality were obtained for each of the studied variables. Principal components analysis and a neural network (NN) algorithm were applied. A high mortality incidence associated to age >70, comorbidities (hypertension, neurological disorders and diabetes), altered vitals such as fever, heart rhythm disturbances or elevated systolic blood pressure, and alterations in several laboratory tests. Remarkably, analysis of therapeutic options either taken individually or in combination drew a universal relationship between the use of Cyclosporine A and better outcomes as also a benefit of tocilizumab and/or corticosteroids in critically ill patients. We present a large Spanish population-based study addressing factors influencing survival in current SARS CoV2 pandemic, with particular emphasis on the effectivity of treatments. In addition, we have generated an NN capable of identifying severity predictors of SARS CoV2. A rapid extraction and management of data protocol from eCR and artificial intelligence in-house implementations allowed us to perform almost real time monitoring of the outbreak evolution.

BackgroundCommunity-acquired pneumonia (CAP) is an acute respiratory condition associated with high mortality in adult populations and is potentially more serious in older patients. Accurate and consistently applied prediction of outcome may contribute to reduce in-hospital mortality. Currently, CAP outcomes are assessed with clinical scores like CURB65, based on signs and symptoms that are non-specific to the disease. Recent literature has shown that machine learning (ML) has the potential to improve outcome prediction, but the sparse and incomplete nature of the data present a challenge for the development of models that can be implemented clinically. MethodsThis study aimed to developed ML models that can support outcome prediction in hospital admissions with CAP using routinely collected and time-dependent data from Leicester hospitals. Thus, by modelling mortality prediction, and predicting URB65 on the third day of admission with the forecast of vital signs, implementing a methodology that explores how different characteristics involved in the training process influence the results of the predictions. ResultsData comprised 9390 admissions in the training set, and 7892 in the validation set, for thirty-four clinical variables (fifteen time-dependent). Results of CAP mortality modelling reported AUC of 0.77 using a GRU model that was trained with the time series of vital signs and blood test. Results also showed improvement in models when balancing classes of the target variable in the training set, as well as improvement when using time dependent data. And importantly when predicting URB65 accuracy of 0.85 was obtained when modelled using GRU, when time series were processed using local scaling. ConclusionsThis approach might represent an opportunity to anticipate adverse outcomes. These results suggest that ML models utilising time series can have sizable impact in the prediction of CAP outcome, from many perspectives: Complementing currently applied scoring systems approaches like CURB65 in hospital settings, prediction of mortality or forecasting the severity of patients from vital signs that have shown correlation with CAP mortality. The models presented require further validation and development, although they present important indication for CAP mortality prediction.

COVID-19 has spread rapidly and caused a global pandemic making it one of the deadliest in history. Early identification of patients with coronavirus disease 2019 who may develop critical illness is of immense importance. Therefore, novel biomarkers were needed to identify patients who will suffer rapid disease progression to severe complications and death. Many treatments were adopted including the antiviral Remdesivir, the antiretroviral Lopinavir /Ritonavir and Tocilizumab. Our study aimed not only to specify high-risk factors and biomarkers of fatal outcome in hospitalized subjects with coronavirus but also to compare the efficacy of the three considered treatments to help clinicians better choose a therapeutic strategy and reduce mortality. We divided the population (n=711) into four main groups based according to the WHO ordinal severity scale. The percentage of mortality, in and out the hospital, the length of stay in the hospital, the pulmonary inflammatory lesion and its distribution, the SARS-CoV-2 IgM and IgG variations at admission, the inflammatory markers, the complete blood count, the coagulation factors and enzymes, proteins and electrolytes profile, glucose and lipid profile, and other relevant markers were measured. The significance of the observed variation was assessed by multivariate and ANOVA analyses. We succeeded to establish a novel predictive scoring model of disease progression based on a cohort of Lebanese hospitalized patients relying on the pulmonary inflammatory lesions, inflammation biomarkers such as LDH, D-Dimer, CRP, IL-6 and the lymphocyte count, the number of comorbidities and the age of the patient which all were significantly correlated with the illness severity showing best outcomes with immunomodulatory and anticoagulant treatments by the results. As top tier, Tocilizumab was more efficient than the two other treatments in non-severe cases but none of the used treatments was insanely effective alone to reduce mortality in severe cases.

Withdrawal StatementThis manuscript has been withdrawn as it was submitted and made public without the full consent of all the authors. Therefore, this work should not be cited as reference for the project. If you have any questions, please contact the corresponding author.

BackgroundAcute Respiratory Distress Syndrome is characterized by the sudden onset of hypoxemia, reduced lung compliance, and bilateral pulmonary infiltrates. We investigated patient demographics and clinical predictors of outcomes in adult patients with ARDS. MethodsWe conducted a retrospective multi-center intensive care unit database study using the eICU Collaborative Research Database (eICU-CRD). The Analysis included 304 patients with ARDS who met all inclusion criteria, including key demographic variables. We Assessed associations between these variables and outcomes, including mortality and length of stay (LoS). ResultsFor each year of increased age, odds of mortality decreased by 4.7% (OR = 0.953, p < 0.001). Higher temperatures were associated with reduced mortality (OR=0.470, p=0.003). Gender (p = 0.596) and BMI (p = 0.964) were not significant predictors of mortality. A multiple linear regression model for predicting ICU length of stay was statistically significant at explaining the variance compared to the null model. High temperatures ({beta} = 0.981, p = 0.015) and low temperatures ({beta} = -1.038, p = 0.006) were significant predictors of LoS, while gender and BMI were not. There was significant association between ethnicity and hospital discharge status ({chi}2(5) = 13.123, p = 0.022), which suggests disparities in outcomes across ethnic groups. ConclusionOur study suggests that increasing age and higher temperatures may serve as protective factors, contrary to the popular belief of age-increasing fragility. Analysis also found that gender and BMI do not affect patient LoS nor mortality. These findings suggest clinicians should consider a patients age and core body temperature when assessing risk of mortality and prognosis, placing less emphasis on BMI and gender.

Background: Patient-ventilator synchrony, an essential prerequisite for non-invasive mechanical ventilation, requires an accurate matching of every phase of the respiration between patient and the ventilator. Methods: We developed a long short-term memory (LSTM)-based model that can predict the inspiratory and expiratory time of the patient. This model consisted of two hidden layers, each with eight LSTM units, and was trained using a dataset of approximately 27000 of 500-ms-long flow signals that captured both inspiratory and expiratory events. Results: The LSTM model achieved 97% accuracy and F1 score in the test data, and the average trigger error was less than 2.20%. In the first trial, 10 volunteers were enrolled. In "Compliance" mode, 78.6% of the triggering by the LSTM model was compatible with neuronal respiration, which was higher than Auto-Trak model (74.2%). Auto-Trak model performed marginally better in the modes of pressure support = 5 and 10 cmH2O. Considering the success in the first clinical trial, we further tested the models by including five patients with acute respiratory distress syndrome (ARDS). The LSTM model exhibited 60.6% of the triggering in the 33%-box, which is better than 49.0% of Auto-Trak model. And the PVI index of the LSTM model was significantly less than Auto-Trak model (36.5% vs 52.9%). Conclusions: Overall, the LSTM model performed comparable to, or even better than, Auto-Trak model in both latency and PVI index. While other mathematical models have been developed, our model was effectively embedded in the chip to control the triggering of ventilator. Trial registration: Approval Number: 2023ZDSYLL348-P01; Approval Date: 28/09/2023. Clinical Trial Registration Number: ChiCTR2500097446; Registration Date: 19/02/2025.

DNA is considered a fundamental component of life, yet it remains vulnerable to damage under extreme conditions, such as ionizing radiation exposure. To better understand this fragility, it becomes important to estimate mutation frequencies under different radiation doses. Furthermore, this approach has potential for future applications, especially in the context of deep space exploration, where astronauts are exposed to higher levels of cosmic radiation. For this purpose, we developed a mathematical model by integrating two existing models, the Monte Carlo Excision Repair (MCER) model and the Whack-a-Mole (WAM) model, both specifically adapted for use in manned space missions. The WAM model is supported with the Monte Carlo simulation to address the lack of human experimental data available in previous studies so that by calculating four key variables related to the human cells defined in the WAM model, potential mutations in astronauts during space exploration were estimated. The results showed small deviations from previous studies, which can be attributed to differences in the type of radiation sources as well as the organisms studied being different from those used in previous studies. With this study, researchers can now better predict mutation frequency during deep space missions by considering the impact of cosmic radiation. This is particularly important in the context of future missions to the Moon and Mars, where cosmic radiation will play an important role in mission planning and risk management.

A total of 6,628 PUBMED-registered publications on the relationships between the effects of vaccination and the provision of micronutrients have been studied by methods of topological analysis of text data. In case of insufficient intake of certain micronutrients, the functioning of the acquired immunity is disrupted resulting in an imbalance of populations of T-cells CD4+/CD8+ and of B-lymphocytes. Nutritional supplements of folate, vitamins A, D and B12, which are recognized regulators of cell division, support a wide range of lymphocyte populations. Trace elements zinc, iron, selenium, manganese and omega-3 polyunsaturated fatty acids are also important for supporting the mechanisms of acquired immunity. The data presented show that a course intake of these micronutrients by patients planning vaccination can significantly improve its effectiveness. In particular, these micronutrients can increase the titers of antibodies to pathogens, and to reduce the percentage of patients who still contract infection after vaccination. Supplements of these micronutrients can also contribute to the safety of vaccination: to prevent malaise and, in the unfortunate case of contracting infection despite the vaccine, to reduce the severity of the course and the mortality from the corresponding infection.

BackgroundPneumonia is a leading cause of death in the United States. Guidelines for its diagnosis and management are periodically updated, but long-term mortality in critically ill patients has not been well studied. ObjectiveTo determine differences in one-year mortality between patients with community-acquired pneumonia (CAP) and healthcare-associated pneumonia or hospital-acquired pneumonia (HCAP/HAP). MethodsA retrospective multivariate analysis of a prospective cohort study that included all patients admitted to a single-center medical intensive care unit (ICU) from October 1, 2007, through September 30, 2008, with a diagnosis of pneumonia. ResultsThere were 181 patients admitted to the medical ICU with a diagnosis of pneumonia, 58.0% with HCAP/HAP and 42.0% with CAP. Those with HCAP/HAP had an older age distribution and higher proportions of cardiovascular (79.1% vs. 63.2%, P=0.02) and neurological (36.2% vs. 18.4%, P=0.01) comorbidities. The HCAP/HAP patients demonstrated an increased risk of death within one a year in the unadjusted analysis (HR 1.6, 95% CI 1.1-2.2, P=0.01) that did not remain significant in the multivariate analysis (HR 1.3, 95% CI 0.80-2.10. P=0.29) when adjusting for simplified acute physiology score (SAPS) II score, age category, source of admission and a history of diabetes mellitus, neurological disease or malignancy. ConclusionCompared to patients admitted to a medical ICU with CAP, those with HCAP/HAP had a higher one-year mortality that is accounted for by the increased co-morbidities associated with a HCAP/HAP diagnosis. Summary of Key PointsO_LIThe American Thoracic Society (ATS) and the Infectious Diseases Society of America (IDSA) developed and periodically update guidelines for the diagnosis and management of community-acquired and nosocomial pneumonia based on the patient-care setting in which pneumonia evolved. ATS/IDSA provides guidelines for empiric antibiotic choices based on the category of pneumonia that is diagnosed. C_LIO_LIPneumonia is a significant cause of mortality in the United States and when combined with influenza ranks as the eighth leading cause of death nationwide yet little is known about the mortality of critically ill patients with pneumonia that require admission to a medical intensive care unit (ICU). C_LIO_LIOur findings suggest that older age, higher severity of illness at ICU admission, and chronic comorbid illnesses are the main contributors to long-term mortality from pneumonia requiring ICU admission. C_LIO_LIIn this cohort, we found an independent association between increased mortality and admission from the general hospital ward rather than directly from the emergency department. C_LIO_LIOur study did not demonstrate that initial guideline-based antibiotic therapy was associated with a reduction in short-term mortality; however, it did demonstrate a high prevalence of resistant pathogens in HCAP/HAP patients, which reflects ATS/IDSA guideline expectations. C_LI

The recognition of Acute Respiratory Distress Syndrome (ARDS) may be delayed or missed entirely among critically ill patients. This study focuses on the development of a predictive algorithm for Hypoxic Respiratory Failure and associated risk of ARDS by utilizing routinely collected bedside monitoring. Specifically, the algorithm aims to predict onset over time. Uniquely, and favorable to robustness, the algorithm utilizes routinely collected, non-invasive cardiorespiratory waveform signals. This is a retrospective, Institutional-Review-Board-approved study of 2,078 patients at a tertiary hospital system. A modified Berlin criteria was used to identify 128 of the patients to have the condition during their encounter. A prediction horizon of 6 to 36 hours was defined for model training and evaluation. Xtreme Gradient Boosting algorithm was evaluated against signal processing and statistical features derived from the waveform and clinical data. Waveform-derived cardiorespiratory features, namely measures relating to variability and multi-scale entropy were robust and reliable features that predicted onset up to 36 hours before the clinical definition is met. The inclusion of structured data from the medical record, namely oxygenation patterns, complete blood counts, and basic metabolics further improved model performance. The combined model with 6-hour prediction horizon achieved an area under the receiver operating characteristic of 0.79 as opposed to the first 24-hour Lung Injury Prediction Score of 0.72.

In the "Space Seed" experiment performed in Kibo module of the International Space Station, growth direction of the inflorescence stem of arabidopsis was examined under space 1 G, G, and ground 1 G conditions in the presence of light. The stems grew almost upright (vertical to the surface of seedbed) under ground 1 G. Although the stems were primarily upright both under space 1 G and G, they tilted slightly. The tilting of the stems under space 1 G was indicated to be due to tilting of the artificial gravitational acceleration vectors produced on the centrifuge. The tilting of the stems under G was suggested to be due to the pressure of directional airflow produced by ventilation.

BackgroundAs a novel disease, understanding the relationship between the clinical and demographic characteristics of coronavirus disease 2019 (COVID-19) patients and their outcome is critical. We investigated this relationship in hospitalized patients in a tertiary healthcare setting. Aims/objectivesTo study COVID-19 severity and outcomes in relation to clinical and demographic characteristics of in admitted patients MethodologyIn this cross-sectional study, medical records for 1087 COVID-19 patients were reviewed to extract symptoms, comorbidities, demographic characteristics, and outcomes data. Statistical analyses included the post-stratification chi-square test, independent sample t-test, multivariate logistic regression, and time-to-event analysis. ResultsThe majority of the study participants were >50 years old (67%) and male (59%) and had the following symptoms: fever (96%), cough (95%), shortness of breath (73%), loss of taste (77%), and loss of smell (77%). Regarding worst outcome, multivariate regression analysis showed that these characteristics were statistically significant: shortness of breath (adjusted odds ratio [aOR] 31.3; 95% CI, 11.87-82.53; p < 0.001), intensive care unit (ICU) admission (aOR 28.3; 95% CI,9.0-89.6; p < 0.001), diabetes mellitus (aOR 5.1; 95% CI;3.2-8.2; p < 0.001), ischemic heart disease (aOR 3.4; 95% CI,1.6-7; p = 0.001), nausea and vomiting (aOR 3.3; 95% CI, 1.7-6.6; p = 0.001), and prolonged hospital stay (aOR 1.04; 95% CI, 1.02-1.08; p = 0.001), while patients with rhinorrhea were significantly protected (aOR 0.3; 95% CI, 0.2-0.5; p < 0.001). A Kaplan-Meier curve showed that the symptoms of shortness of breath, ICU admission, fever, nausea and vomiting, and diarrhea increased the risk of mortality. ConclusionIncreasing age, certain comorbidities and symptoms, and direct admission to the ICU increased the risk of worse outcomes. Further research is needed to determine risk factors that may increase disease severity and devise a proper risk-scoring system to initiate timely management.

COVID-19 is a global pandemic leading high death tolls worldwide day by day. Clinical evidence suggests that COVID-19 patients can be classified as non-severe, severe and critical cases. In particular, studies have highlighted the relationship between the lymphopenia and the severity of the illness, where CD8+ T cells have the lowest levels in critical cases. In this work, we aim to elucidate the key parameters that define the course of the disease deviating from severe to critical case. To this end, several mathematical models are proposed to represent the dynamic of the immune response in patients with SARS-CoV-2 infection. The best model had a good fit to reported experimental data, and in accordance with values found in the literature. Our results suggest that a rapid proliferation of CD8+ T cells is decisive in the severity of the disease.

Glioblastoma aggressively invades surrounding tissue by expressing matrix metalloproteinase-2 (MMP-2). Therefore, effective inhibition of MMP-2 is a desirable target for treatment. In some reports, the chlorotoxin (Ctx) polypeptide produced by the scorpion Leiurus quinquestriatus, interacts with human MMP-2 to inhibit tumor invasion without affecting surrounding tissue. We employed three molecular docking methodologies followed by molecular dynamics simulations to find consensus binding and calculate the binding energy of these peptide ligands to MMP-2. In addition to the Ctx itself, four C-terminal fragments were chosen to study their binding to MMP-2. The molecular docking platforms HPEPDOCK, HADDOCK, and AlphaFold2 created peptide - protein poses for each candidate binding to MMP-2. These poses underwent 500 ns molecular dynamics simulations. Peptide binding on MMP-2 and final binding energies were calculated using the Molecular Mechanics Poisson-Boltzmann Surface Area method. Configurational entropy and root-mean square deviation analyses showed stable peptide - protein complexes. Ctx and its peptide fragments frequently bound to regions on MMP-2 other than the catalytic site. All docking methods shared consensus on large negative binding energies, indicating favorable interaction between Ctx and its analogs with MMP-2. While Ctx and its fragments bind to MMP-2, there is no consensus on which region of MMP-2 they are bound to or which peptide binds strongest. Neither Ctx nor its fragments inhibited MMP-2 enzymatic activity, however, glioblastoma cellular migration was inhibited. Interactions with the non-catalytic regions of MMP-2 suggest allosteric binding to MMP-2. Inhibition of cellular migration without inhibition of MMP-2 activity warrants further study into the possible targets of Ctx expressed in glioblastoma.

Background and objectiveThe outbreak of COVID-19 has become a global health concern. In this study, we evaluate the effectiveness and safety of convalescent plasma therapy in patients with severe and critically ill COVID-19. MethodsSixteen COVID-19 patients received transfusion of anti-COVID-19 antibody-positive convalescent plasma. The main outcome was time for viral nucleic acid amplification (NAA) test turning negative. Clinical laboratory parameters were measured at the baseline (d0) before plasma transfusion, and day 1 (d1), day 3 (d3) after transfusion as well. ResultsAmong the 16 patients, 10 of them had a consistently positive result of viral NAA test before convalescent plasma transfusion. Eight patients (8/10) became negative from day 2 to day 8 after transfusion. Severe patients showed a shorter time for NAA test turning negative after transfusion (mean rank 2.17 vs 5{middle dot}90, P = 0.036). Two critically ill patients transfused plasma with lower antibody level remained a positive result of NAA test. CRP level demonstrated a decline 1 day after convalescent plasma treatment, compared with the baseline (P = 0.017). No adverse events were observed during convalescent plasma transfusion. ConclusionsViral NAA test of most patients with COVID-19 who received convalescent plasma transfusion turned negative on the 2nd to 8th days after transfusion, and the negative time of severe patients was shorter than that of critically ill patients. Trial RegistrationChinese Clinical Trial Registry; No.: ChiCTR2000030627 URL:http://www.chictr.org